Preamble

In this course we take you through a focused establishment of R, there are many courses available that you can take to further improve your skills with R programming but the focus here is to enable you to use this language comfortably for your health data science projects. We will cover:

  • Set up
  • Importing data
  • Data Analysis
  • Statistical inference
  • Comparison of means
  • Analysis of Variance
  • Correlation
  • Regression
  • Prediction
  • Model comparison
  • Statistical power

Set up

Importing data

Data Analysis

Statistical inference

Comparison of means

Analysis of Variance

Correlation

Regression

Prediction

Model comparison

Statistical power

Report structure

Abstract

Provide the reader with a succinct summary of your work

Introduction

Provide an introduction to you portfolio to reader.

Method

covering data access requirements, ethics, metadata and all methodological aspects of your project

Results

Use this section to showcase the results of your data manipulation that will contribute to the project

Conclusion

Summaries your findings,discuss them in the context of other similar work or questions and suggestions for future work. Conclude your portfolio with what started your data exploration and what have the data contributed in the decisions for patient care or health service delivery.

Data Visualisation

Area graph

## `geom_smooth()` using formula = 'y ~ x'
## Setting the `off` event (i.e., 'plotly_doubleclick') to match the `on` event (i.e., 'plotly_hover'). You can change this default via the `highlight()` function.

Area graph

## line plot

Scatterplots

Animated graphs

Sometimes even with interactive graphs, the picture is not complete. Adding movements to a complex graph can help make the relationship between the variables more transparent and add more meaning to the graphs. To achieve this, we use animations.

Bubble graphs

A template bubble graph using the airquality dataset in R.

Metadata and data tables

defining a dataframe

MM.HDS <- data.frame(
  Courses = c("Course 1","Course 2","Course 3","Course 4","Course 5"), 
  Unit_Titles=c(
    "Harnessing data for healthcare advancement",
    "The impact of big data on healthcare",
    "Mastering critical analysis in evidence-based healthcare",
    "Navigating complex health data challenges",
    "Capstone Assignment"
  ),
  Indicative_Learning_hours = c(
    "30 hrs",
    "30 hrs",
    "30 hrs",
    "30 hrs",
    "30 hrs"
  ))

kable(MM.HDS)
Courses Unit_Titles Indicative_Learning_hours
Course 1 Harnessing data for healthcare advancement 30 hrs
Course 2 The impact of big data on healthcare 30 hrs
Course 3 Mastering critical analysis in evidence-based healthcare 30 hrs
Course 4 Navigating complex health data challenges 30 hrs
Course 5 Capstone Assignment 30 hrs

Interactive data elements

DT::datatable(
  MM.HDS,
  extensions = 'Buttons',
  options = list(
              paging = TRUE,
              searching = TRUE,
              fixedColumns = TRUE,
              autoWidth = TRUE,
              ordering = TRUE,
              dom = 'tB',
              buttons = c('copy', 'excel')
              ),
              class = "display"
)

Loading an existing data frame